Overview

Dataset statistics

Number of variables24
Number of observations397
Missing cells740
Missing cells (%)7.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory61.0 KiB
Average record size in memory157.3 B

Variable types

Categorical9
Boolean6
Numeric8
Unsupported1

Alerts

isPb has constant value "True" Constant
punctuation has constant value "False" Constant
numbers has constant value "False" Constant
difficulty has constant value "normal" Constant
lazyMode has constant value "False" Constant
blindMode has constant value "False" Constant
bailedOut has constant value "False" Constant
_id has a high cardinality: 397 distinct values High cardinality
charStats has a high cardinality: 336 distinct values High cardinality
wpm is highly correlated with acc and 2 other fieldsHigh correlation
acc is highly correlated with wpmHigh correlation
rawWpm is highly correlated with wpm and 1 other fieldsHigh correlation
restartCount is highly correlated with incompleteTestSecondsHigh correlation
testDuration is highly correlated with wpm and 1 other fieldsHigh correlation
incompleteTestSeconds is highly correlated with restartCountHigh correlation
wpm is highly correlated with rawWpm and 2 other fieldsHigh correlation
rawWpm is highly correlated with wpm and 2 other fieldsHigh correlation
restartCount is highly correlated with incompleteTestSecondsHigh correlation
testDuration is highly correlated with wpm and 1 other fieldsHigh correlation
incompleteTestSeconds is highly correlated with restartCountHigh correlation
timestamp is highly correlated with wpm and 1 other fieldsHigh correlation
wpm is highly correlated with rawWpmHigh correlation
rawWpm is highly correlated with wpmHigh correlation
restartCount is highly correlated with incompleteTestSecondsHigh correlation
incompleteTestSeconds is highly correlated with restartCountHigh correlation
isPb is highly correlated with numbers and 11 other fieldsHigh correlation
numbers is highly correlated with isPb and 11 other fieldsHigh correlation
mode2 is highly correlated with isPb and 8 other fieldsHigh correlation
difficulty is highly correlated with isPb and 11 other fieldsHigh correlation
afkDuration is highly correlated with isPb and 6 other fieldsHigh correlation
quoteLength is highly correlated with isPb and 8 other fieldsHigh correlation
language is highly correlated with isPb and 6 other fieldsHigh correlation
bailedOut is highly correlated with isPb and 11 other fieldsHigh correlation
blindMode is highly correlated with isPb and 11 other fieldsHigh correlation
funbox is highly correlated with isPb and 6 other fieldsHigh correlation
lazyMode is highly correlated with isPb and 11 other fieldsHigh correlation
punctuation is highly correlated with isPb and 11 other fieldsHigh correlation
mode is highly correlated with isPb and 8 other fieldsHigh correlation
wpm is highly correlated with acc and 8 other fieldsHigh correlation
acc is highly correlated with wpm and 3 other fieldsHigh correlation
rawWpm is highly correlated with wpm and 8 other fieldsHigh correlation
consistency is highly correlated with wpm and 6 other fieldsHigh correlation
mode is highly correlated with wpm and 6 other fieldsHigh correlation
mode2 is highly correlated with wpm and 6 other fieldsHigh correlation
quoteLength is highly correlated with mode and 3 other fieldsHigh correlation
restartCount is highly correlated with incompleteTestSecondsHigh correlation
testDuration is highly correlated with wpm and 5 other fieldsHigh correlation
afkDuration is highly correlated with wpm and 4 other fieldsHigh correlation
incompleteTestSeconds is highly correlated with restartCountHigh correlation
language is highly correlated with wpm and 4 other fieldsHigh correlation
timestamp is highly correlated with wpm and 6 other fieldsHigh correlation
isPb has 343 (86.4%) missing values Missing
tags has 397 (100.0%) missing values Missing
_id is uniformly distributed Uniform
charStats is uniformly distributed Uniform
_id has unique values Unique
timestamp has unique values Unique
tags is an unsupported type, check if it needs cleaning or further analysis Unsupported
restartCount has 89 (22.4%) zeros Zeros
incompleteTestSeconds has 89 (22.4%) zeros Zeros

Reproduction

Analysis started2023-04-06 03:34:23.495576
Analysis finished2023-04-06 03:34:47.577328
Duration24.08 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

_id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct397
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
64164c67fe979dd89a73860b
 
1
6223c0e9be7cd1cc6594ac1b
 
1
6223c117be7cd1cc6594ad0f
 
1
6223c12ebe7cd1cc6594ad83
 
1
6223c14bbe7cd1cc659c792b
 
1
Other values (392)
392 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters9528
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique397 ?
Unique (%)100.0%

Sample

1st row64164c67fe979dd89a73860b
2nd row64164c29fe979dd89a738515
3rd row641647b6fe979dd89a6d9f6b
4th row6416476efe979dd89a6d9e0d
5th row6416472dfe979dd89a6d9cc3

Common Values

ValueCountFrequency (%)
64164c67fe979dd89a73860b1
 
0.3%
6223c0e9be7cd1cc6594ac1b1
 
0.3%
6223c117be7cd1cc6594ad0f1
 
0.3%
6223c12ebe7cd1cc6594ad831
 
0.3%
6223c14bbe7cd1cc659c792b1
 
0.3%
6223c189be7cd1cc659c7a661
 
0.3%
6223c195be7cd1cc659c7aa21
 
0.3%
6223c1a1be7cd1cc659c7ae51
 
0.3%
6223c1b1be7cd1cc659c7b461
 
0.3%
6223c1c2be7cd1cc659c7b911
 
0.3%
Other values (387)387
97.5%

Length

2023-04-06T03:34:47.754686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
64164c67fe979dd89a73860b1
 
0.3%
63f53f82c283c730c37daa001
 
0.3%
641647b6fe979dd89a6d9f6b1
 
0.3%
6416476efe979dd89a6d9e0d1
 
0.3%
6416472dfe979dd89a6d9cc31
 
0.3%
641646eefe979dd89a6d9b7c1
 
0.3%
64164655fe979dd89a6d98ec1
 
0.3%
64164611fe979dd89a6d97991
 
0.3%
641645d3fe979dd89a67c5a81
 
0.3%
6416455bfe979dd89a67c36f1
 
0.3%
Other values (387)387
97.5%

Most occurring characters

ValueCountFrequency (%)
21076
 
11.3%
6962
 
10.1%
3766
 
8.0%
c618
 
6.5%
a587
 
6.2%
b587
 
6.2%
d557
 
5.8%
5549
 
5.8%
1547
 
5.7%
7524
 
5.5%
Other values (6)2755
28.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6259
65.7%
Lowercase Letter3269
34.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
21076
17.2%
6962
15.4%
3766
12.2%
5549
8.8%
1547
8.7%
7524
8.4%
0481
7.7%
8480
7.7%
9440
7.0%
4434
6.9%
Lowercase Letter
ValueCountFrequency (%)
c618
18.9%
a587
18.0%
b587
18.0%
d557
17.0%
f506
15.5%
e414
12.7%

Most occurring scripts

ValueCountFrequency (%)
Common6259
65.7%
Latin3269
34.3%

Most frequent character per script

Common
ValueCountFrequency (%)
21076
17.2%
6962
15.4%
3766
12.2%
5549
8.8%
1547
8.7%
7524
8.4%
0481
7.7%
8480
7.7%
9440
7.0%
4434
6.9%
Latin
ValueCountFrequency (%)
c618
18.9%
a587
18.0%
b587
18.0%
d557
17.0%
f506
15.5%
e414
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII9528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21076
 
11.3%
6962
 
10.1%
3766
 
8.0%
c618
 
6.5%
a587
 
6.2%
b587
 
6.2%
d557
 
5.8%
5549
 
5.8%
1547
 
5.7%
7524
 
5.5%
Other values (6)2755
28.9%

isPb
Boolean

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)1.9%
Missing343
Missing (%)86.4%
Memory size3.2 KiB
True
54 
(Missing)
343 
ValueCountFrequency (%)
True54
 
13.6%
(Missing)343
86.4%
2023-04-06T03:34:47.994484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

wpm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct330
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.24130982
Minimum20.6
Maximum151.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-06T03:34:48.226363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20.6
5-th percentile56.95
Q193.54
median100.51
Q3106.92
95-th percentile123.586
Maximum151.72
Range131.12
Interquartile range (IQR)13.38

Descriptive statistics

Standard deviation19.41616853
Coefficient of variation (CV)0.1996699609
Kurtosis1.949160992
Mean97.24130982
Median Absolute Deviation (MAD)6.69
Skewness-0.9522172454
Sum38604.8
Variance376.9876003
MonotonicityNot monotonic
2023-04-06T03:34:48.534851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103.139
 
2.3%
103.26
 
1.5%
100.85
 
1.3%
99.134
 
1.0%
99.574
 
1.0%
1044
 
1.0%
105.534
 
1.0%
101.533
 
0.8%
110.333
 
0.8%
102.333
 
0.8%
Other values (320)352
88.7%
ValueCountFrequency (%)
20.61
0.3%
301
0.3%
31.21
0.3%
37.391
0.3%
37.591
0.3%
39.61
0.3%
46.371
0.3%
46.781
0.3%
47.581
0.3%
49.21
0.3%
ValueCountFrequency (%)
151.721
0.3%
151.661
0.3%
151.591
0.3%
1471
0.3%
138.21
0.3%
133.891
0.3%
132.81
0.3%
132.131
0.3%
131.741
0.3%
131.351
0.3%

acc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct265
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.6772796
Minimum78.62
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-06T03:34:48.842426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum78.62
5-th percentile90.04
Q193.89
median96.06
Q398.16
95-th percentile100
Maximum100
Range21.38
Interquartile range (IQR)4.27

Descriptive statistics

Standard deviation3.35745923
Coefficient of variation (CV)0.03509149972
Kurtosis3.339286776
Mean95.6772796
Median Absolute Deviation (MAD)2.13
Skewness-1.263852512
Sum37983.88
Variance11.27253248
MonotonicityNot monotonic
2023-04-06T03:34:49.148182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10052
 
13.1%
97.874
 
1.0%
98.374
 
1.0%
97.734
 
1.0%
99.244
 
1.0%
96.184
 
1.0%
94.784
 
1.0%
95.243
 
0.8%
94.123
 
0.8%
95.563
 
0.8%
Other values (255)312
78.6%
ValueCountFrequency (%)
78.621
0.3%
81.032
0.5%
83.081
0.3%
83.331
0.3%
84.091
0.3%
86.131
0.3%
87.51
0.3%
87.841
0.3%
88.461
0.3%
88.971
0.3%
ValueCountFrequency (%)
10052
13.1%
99.381
 
0.3%
99.311
 
0.3%
99.281
 
0.3%
99.261
 
0.3%
99.251
 
0.3%
99.244
 
1.0%
99.222
 
0.5%
99.211
 
0.3%
99.21
 
0.3%

rawWpm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct330
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.9043829
Minimum26
Maximum155.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-06T03:34:49.443405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile60.464
Q1101.13
median107.93
Q3114.32
95-th percentile128.76
Maximum155.32
Range129.32
Interquartile range (IQR)13.19

Descriptive statistics

Standard deviation20.33031512
Coefficient of variation (CV)0.1956636916
Kurtosis1.983240578
Mean103.9043829
Median Absolute Deviation (MAD)6.47
Skewness-1.139838973
Sum41250.04
Variance413.3217131
MonotonicityNot monotonic
2023-04-06T03:34:49.757072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107.25
 
1.3%
115.125
 
1.3%
1044
 
1.0%
105.64
 
1.0%
105.534
 
1.0%
103.134
 
1.0%
119.923
 
0.8%
102.373
 
0.8%
118.323
 
0.8%
104.733
 
0.8%
Other values (320)359
90.4%
ValueCountFrequency (%)
261
0.3%
32.591
0.3%
33.81
0.3%
38.591
0.3%
41.991
0.3%
42.41
0.3%
50.782
0.5%
53.181
0.3%
54.361
0.3%
55.591
0.3%
ValueCountFrequency (%)
155.321
0.3%
154.871
0.3%
153.241
0.3%
151.721
0.3%
151.591
0.3%
150.991
0.3%
1471
0.3%
145.881
0.3%
137.271
0.3%
133.891
0.3%

consistency
Real number (ℝ≥0)

HIGH CORRELATION

Distinct360
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.37891688
Minimum23.1
Maximum94.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-06T03:34:50.052098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum23.1
5-th percentile57.35
Q169.13
median75.84
Q380.77
95-th percentile87.484
Maximum94.95
Range71.85
Interquartile range (IQR)11.64

Descriptive statistics

Standard deviation9.441206939
Coefficient of variation (CV)0.1269339127
Kurtosis2.715553232
Mean74.37891688
Median Absolute Deviation (MAD)5.74
Skewness-1.061635895
Sum29528.43
Variance89.13638847
MonotonicityNot monotonic
2023-04-06T03:34:50.367007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.893
 
0.8%
78.923
 
0.8%
74.493
 
0.8%
77.072
 
0.5%
81.382
 
0.5%
74.532
 
0.5%
80.92
 
0.5%
83.822
 
0.5%
81.862
 
0.5%
83.32
 
0.5%
Other values (350)374
94.2%
ValueCountFrequency (%)
23.11
0.3%
34.131
0.3%
43.51
0.3%
44.951
0.3%
48.551
0.3%
49.571
0.3%
50.511
0.3%
51.481
0.3%
52.041
0.3%
52.431
0.3%
ValueCountFrequency (%)
94.951
0.3%
94.91
0.3%
94.251
0.3%
92.441
0.3%
92.361
0.3%
91.751
0.3%
91.231
0.3%
90.381
0.3%
89.381
0.3%
89.051
0.3%

charStats
Categorical

HIGH CARDINALITY
UNIFORM

Distinct336
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
129,0,0,0
 
5
46,0,0,0
 
4
53,0,0,0
 
4
44,0,0,0
 
3
127,0,0,0
 
3
Other values (331)
378 

Length

Max length10
Median length9
Mean length8.816120907
Min length8

Characters and Unicode

Total characters3500
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique294 ?
Unique (%)74.1%

Sample

1st row314,2,0,0
2nd row316,3,0,1
3rd row188,3,0,0
4th row198,3,1,0
5th row187,1,0,0

Common Values

ValueCountFrequency (%)
129,0,0,05
 
1.3%
46,0,0,04
 
1.0%
53,0,0,04
 
1.0%
44,0,0,03
 
0.8%
127,0,0,03
 
0.8%
164,0,0,03
 
0.8%
47,0,0,03
 
0.8%
45,0,0,03
 
0.8%
73,0,0,03
 
0.8%
52,0,0,03
 
0.8%
Other values (326)363
91.4%

Length

2023-04-06T03:34:50.685076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
129,0,0,05
 
1.3%
53,0,0,04
 
1.0%
46,0,0,04
 
1.0%
52,0,0,03
 
0.8%
49,0,0,03
 
0.8%
80,0,0,03
 
0.8%
42,0,0,03
 
0.8%
131,0,0,03
 
0.8%
48,0,0,03
 
0.8%
73,0,0,03
 
0.8%
Other values (326)363
91.4%

Most occurring characters

ValueCountFrequency (%)
,1191
34.0%
0683
19.5%
1519
14.8%
2318
 
9.1%
3215
 
6.1%
4159
 
4.5%
5108
 
3.1%
685
 
2.4%
877
 
2.2%
974
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2309
66.0%
Other Punctuation1191
34.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0683
29.6%
1519
22.5%
2318
13.8%
3215
 
9.3%
4159
 
6.9%
5108
 
4.7%
685
 
3.7%
877
 
3.3%
974
 
3.2%
771
 
3.1%
Other Punctuation
ValueCountFrequency (%)
,1191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
,1191
34.0%
0683
19.5%
1519
14.8%
2318
 
9.1%
3215
 
6.1%
4159
 
4.5%
5108
 
3.1%
685
 
2.4%
877
 
2.2%
974
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,1191
34.0%
0683
19.5%
1519
14.8%
2318
 
9.1%
3215
 
6.1%
4159
 
4.5%
5108
 
3.1%
685
 
2.4%
877
 
2.2%
974
 
2.1%

mode
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
time
202 
words
137 
quote
43 
custom
 
12
zen
 
3

Length

Max length6
Median length4
Mean length4.506297229
Min length3

Characters and Unicode

Total characters1789
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtime
2nd rowtime
3rd rowtime
4th rowtime
5th rowtime

Common Values

ValueCountFrequency (%)
time202
50.9%
words137
34.5%
quote43
 
10.8%
custom12
 
3.0%
zen3
 
0.8%

Length

2023-04-06T03:34:50.964480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-06T03:34:51.271234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
time202
50.9%
words137
34.5%
quote43
 
10.8%
custom12
 
3.0%
zen3
 
0.8%

Most occurring characters

ValueCountFrequency (%)
t257
14.4%
e248
13.9%
m214
12.0%
i202
11.3%
o192
10.7%
s149
8.3%
w137
7.7%
r137
7.7%
d137
7.7%
u55
 
3.1%
Other values (4)61
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1789
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t257
14.4%
e248
13.9%
m214
12.0%
i202
11.3%
o192
10.7%
s149
8.3%
w137
7.7%
r137
7.7%
d137
7.7%
u55
 
3.1%
Other values (4)61
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin1789
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t257
14.4%
e248
13.9%
m214
12.0%
i202
11.3%
o192
10.7%
s149
8.3%
w137
7.7%
r137
7.7%
d137
7.7%
u55
 
3.1%
Other values (4)61
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1789
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t257
14.4%
e248
13.9%
m214
12.0%
i202
11.3%
o192
10.7%
s149
8.3%
w137
7.7%
r137
7.7%
d137
7.7%
u55
 
3.1%
Other values (4)61
 
3.4%

mode2
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct46
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
30
91 
15
82 
25
78 
10
48 
60
28 
Other values (41)
70 

Length

Max length6
Median length2
Mean length2.297229219
Min length1

Characters and Unicode

Total characters912
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)8.3%

Sample

1st row60
2nd row60
3rd row60
4th row60
5th row60

Common Values

ValueCountFrequency (%)
3091
22.9%
1582
20.7%
2578
19.6%
1048
12.1%
6028
 
7.1%
custom12
 
3.0%
506
 
1.5%
1006
 
1.5%
624
 
1.0%
zen3
 
0.8%
Other values (36)39
9.8%

Length

2023-04-06T03:34:51.567677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3091
22.9%
1582
20.7%
2578
19.6%
1048
12.1%
6028
 
7.1%
custom12
 
3.0%
506
 
1.5%
1006
 
1.5%
624
 
1.0%
zen3
 
0.8%
Other values (36)39
9.8%

Most occurring characters

ValueCountFrequency (%)
0199
21.8%
5191
20.9%
1148
16.2%
2104
11.4%
399
10.9%
642
 
4.6%
423
 
2.5%
m12
 
1.3%
o12
 
1.3%
t12
 
1.3%
Other values (9)70
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number831
91.1%
Lowercase Letter81
 
8.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0199
23.9%
5191
23.0%
1148
17.8%
2104
12.5%
399
11.9%
642
 
5.1%
423
 
2.8%
710
 
1.2%
98
 
1.0%
87
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
m12
14.8%
o12
14.8%
t12
14.8%
s12
14.8%
u12
14.8%
c12
14.8%
z3
 
3.7%
e3
 
3.7%
n3
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common831
91.1%
Latin81
 
8.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0199
23.9%
5191
23.0%
1148
17.8%
2104
12.5%
399
11.9%
642
 
5.1%
423
 
2.8%
710
 
1.2%
98
 
1.0%
87
 
0.8%
Latin
ValueCountFrequency (%)
m12
14.8%
o12
14.8%
t12
14.8%
s12
14.8%
u12
14.8%
c12
14.8%
z3
 
3.7%
e3
 
3.7%
n3
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII912
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0199
21.8%
5191
20.9%
1148
16.2%
2104
11.4%
399
10.9%
642
 
4.6%
423
 
2.5%
m12
 
1.3%
o12
 
1.3%
t12
 
1.3%
Other values (9)70
 
7.7%

quoteLength
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
-1
354 
0
43 

Length

Max length2
Median length2
Mean length1.891687657
Min length1

Characters and Unicode

Total characters751
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1354
89.2%
043
 
10.8%

Length

2023-04-06T03:34:51.822193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-06T03:34:52.076046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1354
89.2%
043
 
10.8%

Most occurring characters

ValueCountFrequency (%)
-354
47.1%
1354
47.1%
043
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number397
52.9%
Dash Punctuation354
47.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1354
89.2%
043
 
10.8%
Dash Punctuation
ValueCountFrequency (%)
-354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common751
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-354
47.1%
1354
47.1%
043
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII751
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-354
47.1%
1354
47.1%
043
 
5.7%

restartCount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.901763224
Minimum0
Maximum78
Zeros89
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-06T03:34:52.316319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile20.2
Maximum78
Range78
Interquartile range (IQR)6

Descriptive statistics

Standard deviation10.02637541
Coefficient of variation (CV)1.698877951
Kurtosis23.80543994
Mean5.901763224
Median Absolute Deviation (MAD)3
Skewness4.250058677
Sum2343
Variance100.528204
MonotonicityNot monotonic
2023-04-06T03:34:52.602217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
089
22.4%
165
16.4%
239
9.8%
432
 
8.1%
331
 
7.8%
619
 
4.8%
517
 
4.3%
715
 
3.8%
812
 
3.0%
159
 
2.3%
Other values (27)69
17.4%
ValueCountFrequency (%)
089
22.4%
165
16.4%
239
9.8%
331
 
7.8%
432
 
8.1%
517
 
4.3%
619
 
4.8%
715
 
3.8%
812
 
3.0%
94
 
1.0%
ValueCountFrequency (%)
781
0.3%
772
0.5%
681
0.3%
521
0.3%
501
0.3%
351
0.3%
341
0.3%
331
0.3%
311
0.3%
291
0.3%

testDuration
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct194
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.48453904
Minimum3.289
Maximum120.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-06T03:34:52.901692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.289
5-th percentile4.612
Q110.3
median15.01
Q330.01
95-th percentile60.01
Maximum120.01
Range116.721
Interquartile range (IQR)19.71

Descriptive statistics

Standard deviation15.79248172
Coefficient of variation (CV)0.7709464042
Kurtosis4.724555056
Mean20.48453904
Median Absolute Deviation (MAD)8.23
Skewness1.834783564
Sum8132.362
Variance249.4024788
MonotonicityNot monotonic
2023-04-06T03:34:53.270599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.0152
 
13.1%
30.0150
 
12.6%
3027
 
6.8%
1521
 
5.3%
30.0214
 
3.5%
60.0114
 
3.5%
15.028
 
2.0%
608
 
2.0%
60.026
 
1.5%
13.732
 
0.5%
Other values (184)195
49.1%
ValueCountFrequency (%)
3.2891
0.3%
3.3221
0.3%
3.4551
0.3%
3.7551
0.3%
3.8391
0.3%
3.991
0.3%
4.0591
0.3%
4.181
0.3%
4.21
0.3%
4.371
0.3%
ValueCountFrequency (%)
120.011
 
0.3%
64.031
 
0.3%
63.341
 
0.3%
61.721
 
0.3%
60.026
1.5%
60.0114
3.5%
608
2.0%
59.691
 
0.3%
57.371
 
0.3%
56.871
 
0.3%

afkDuration
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
0
387 
1
 
7
2
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters397
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row0
2nd row0
3rd row0
4th row2
5th row1

Common Values

ValueCountFrequency (%)
0387
97.5%
17
 
1.8%
22
 
0.5%
31
 
0.3%

Length

2023-04-06T03:34:53.559329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-06T03:34:53.825857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0387
97.5%
17
 
1.8%
22
 
0.5%
31
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0387
97.5%
17
 
1.8%
22
 
0.5%
31
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number397
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0387
97.5%
17
 
1.8%
22
 
0.5%
31
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common397
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0387
97.5%
17
 
1.8%
22
 
0.5%
31
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII397
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0387
97.5%
17
 
1.8%
22
 
0.5%
31
 
0.3%

incompleteTestSeconds
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct305
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.9215869
Minimum0
Maximum473.49
Zeros89
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-06T03:34:54.065018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.99
median12.97
Q340.09
95-th percentile114.476
Maximum473.49
Range473.49
Interquartile range (IQR)39.1

Descriptive statistics

Standard deviation58.84040799
Coefficient of variation (CV)1.787289542
Kurtosis24.58493444
Mean32.9215869
Median Absolute Deviation (MAD)12.97
Skewness4.313385215
Sum13069.87
Variance3462.193612
MonotonicityNot monotonic
2023-04-06T03:34:54.363747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
089
 
22.4%
8.222
 
0.5%
11.662
 
0.5%
24.172
 
0.5%
2.922
 
0.5%
6.971
 
0.3%
16.411
 
0.3%
39.981
 
0.3%
3.191
 
0.3%
4.041
 
0.3%
Other values (295)295
74.3%
ValueCountFrequency (%)
089
22.4%
0.031
 
0.3%
0.041
 
0.3%
0.051
 
0.3%
0.121
 
0.3%
0.21
 
0.3%
0.421
 
0.3%
0.711
 
0.3%
0.811
 
0.3%
0.851
 
0.3%
ValueCountFrequency (%)
473.491
0.3%
462.961
0.3%
457.971
0.3%
354.491
0.3%
276.991
0.3%
262.11
0.3%
230.531
0.3%
229.651
0.3%
199.271
0.3%
196.411
0.3%

punctuation
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size525.0 B
False
397 
ValueCountFrequency (%)
False397
100.0%
2023-04-06T03:34:54.635401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

numbers
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size525.0 B
False
397 
ValueCountFrequency (%)
False397
100.0%
2023-04-06T03:34:54.842125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

language
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
english
340 
swedish
 
34
norwegian
 
8
german
 
7
icelandic_1k
 
4
Other values (2)
 
4

Length

Max length18
Median length7
Mean length7.118387909
Min length5

Characters and Unicode

Total characters2826
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowswedish
2nd rowswedish
3rd rowicelandic_1k
4th rowicelandic_1k
5th rowicelandic_1k

Common Values

ValueCountFrequency (%)
english340
85.6%
swedish34
 
8.6%
norwegian8
 
2.0%
german7
 
1.8%
icelandic_1k4
 
1.0%
swedish_diacritics2
 
0.5%
malay2
 
0.5%

Length

2023-04-06T03:34:55.056961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-06T03:34:55.373997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
english340
85.6%
swedish34
 
8.6%
norwegian8
 
2.0%
german7
 
1.8%
icelandic_1k4
 
1.0%
swedish_diacritics2
 
0.5%
malay2
 
0.5%

Most occurring characters

ValueCountFrequency (%)
s414
14.6%
i398
14.1%
e395
14.0%
h376
13.3%
n367
13.0%
g355
12.6%
l346
12.2%
w44
 
1.6%
d42
 
1.5%
a25
 
0.9%
Other values (9)64
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2816
99.6%
Connector Punctuation6
 
0.2%
Decimal Number4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s414
14.7%
i398
14.1%
e395
14.0%
h376
13.4%
n367
13.0%
g355
12.6%
l346
12.3%
w44
 
1.6%
d42
 
1.5%
a25
 
0.9%
Other values (7)54
 
1.9%
Connector Punctuation
ValueCountFrequency (%)
_6
100.0%
Decimal Number
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2816
99.6%
Common10
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
s414
14.7%
i398
14.1%
e395
14.0%
h376
13.4%
n367
13.0%
g355
12.6%
l346
12.3%
w44
 
1.6%
d42
 
1.5%
a25
 
0.9%
Other values (7)54
 
1.9%
Common
ValueCountFrequency (%)
_6
60.0%
14
40.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2826
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s414
14.6%
i398
14.1%
e395
14.0%
h376
13.3%
n367
13.0%
g355
12.6%
l346
12.2%
w44
 
1.6%
d42
 
1.5%
a25
 
0.9%
Other values (9)64
 
2.3%

funbox
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
none
395 
nausea
 
2

Length

Max length6
Median length4
Mean length4.010075567
Min length4

Characters and Unicode

Total characters1592
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownone
2nd rownone
3rd rownone
4th rownone
5th rownone

Common Values

ValueCountFrequency (%)
none395
99.5%
nausea2
 
0.5%

Length

2023-04-06T03:34:56.110230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-06T03:34:56.601664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
none395
99.5%
nausea2
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n792
49.7%
e397
24.9%
o395
24.8%
a4
 
0.3%
u2
 
0.1%
s2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1592
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n792
49.7%
e397
24.9%
o395
24.8%
a4
 
0.3%
u2
 
0.1%
s2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n792
49.7%
e397
24.9%
o395
24.8%
a4
 
0.3%
u2
 
0.1%
s2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n792
49.7%
e397
24.9%
o395
24.8%
a4
 
0.3%
u2
 
0.1%
s2
 
0.1%

difficulty
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
normal
397 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters2382
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal397
100.0%

Length

2023-04-06T03:34:56.916804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-06T03:34:57.282825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
normal397
100.0%

Most occurring characters

ValueCountFrequency (%)
n397
16.7%
o397
16.7%
r397
16.7%
m397
16.7%
a397
16.7%
l397
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2382
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n397
16.7%
o397
16.7%
r397
16.7%
m397
16.7%
a397
16.7%
l397
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin2382
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n397
16.7%
o397
16.7%
r397
16.7%
m397
16.7%
a397
16.7%
l397
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2382
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n397
16.7%
o397
16.7%
r397
16.7%
m397
16.7%
a397
16.7%
l397
16.7%

lazyMode
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size525.0 B
False
397 
ValueCountFrequency (%)
False397
100.0%
2023-04-06T03:34:57.686871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

blindMode
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size525.0 B
False
397 
ValueCountFrequency (%)
False397
100.0%
2023-04-06T03:34:58.058925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

bailedOut
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size525.0 B
False
397 
ValueCountFrequency (%)
False397
100.0%
2023-04-06T03:34:58.270446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

tags
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing397
Missing (%)100.0%
Memory size3.2 KiB

timestamp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct397
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.652297083 × 1012
Minimum1.644346621 × 1012
Maximum1.679182951 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-06T03:34:58.501029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.644346621 × 1012
5-th percentile1.646085128 × 1012
Q11.646507677 × 1012
median1.647493984 × 1012
Q31.650147067 × 1012
95-th percentile1.677017238 × 1012
Maximum1.679182951 × 1012
Range3.483633 × 1010
Interquartile range (IQR)3639390000

Descriptive statistics

Standard deviation1.022840478 × 1010
Coefficient of variation (CV)0.006190415078
Kurtosis1.615473025
Mean1.652297083 × 1012
Median Absolute Deviation (MAD)1378075000
Skewness1.794966806
Sum6.559619421 × 1014
Variance1.046202643 × 1020
MonotonicityStrictly decreasing
2023-04-06T03:34:58.805718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.679182951 × 10121
 
0.3%
1.646510314 × 10121
 
0.3%
1.646510359 × 10121
 
0.3%
1.646510382 × 10121
 
0.3%
1.646510411 × 10121
 
0.3%
1.646510474 × 10121
 
0.3%
1.646510485 × 10121
 
0.3%
1.646510498 × 10121
 
0.3%
1.646510514 × 10121
 
0.3%
1.64651053 × 10121
 
0.3%
Other values (387)387
97.5%
ValueCountFrequency (%)
1.644346621 × 10121
0.3%
1.644346799 × 10121
0.3%
1.644347237 × 10121
0.3%
1.644347761 × 10121
0.3%
1.645497323 × 10121
0.3%
1.645497475 × 10121
0.3%
1.645497565 × 10121
0.3%
1.645497615 × 10121
0.3%
1.645497666 × 10121
0.3%
1.645497717 × 10121
0.3%
ValueCountFrequency (%)
1.679182951 × 10121
0.3%
1.679182889 × 10121
0.3%
1.67918175 × 10121
0.3%
1.679181678 × 10121
0.3%
1.679181613 × 10121
0.3%
1.67918155 × 10121
0.3%
1.679181397 × 10121
0.3%
1.679181329 × 10121
0.3%
1.679181267 × 10121
0.3%
1.679181147 × 10121
0.3%

Interactions

2023-04-06T03:34:42.503546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:26.911855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:29.595858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:32.097502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:34.370344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:36.360292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:38.600360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:40.566423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:42.753812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:27.157158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:29.833286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:32.497320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:34.605303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:36.829577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:38.852344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:40.794977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:43.029693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:27.379941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:30.068972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:32.874531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:34.844868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:37.063155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:39.070967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:41.027828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:43.430105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:27.632769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:30.340478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:33.134176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:35.106937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:37.334804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:39.320372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:41.269250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:43.851428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:27.875236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:30.666552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:33.403125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:35.378695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:37.591044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:39.576727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:41.511800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:44.238387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:28.878889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:31.047552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:33.661585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:35.629258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:37.864400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:39.840107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:41.780419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:44.603502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:29.114413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:31.427338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:33.891729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:35.877542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:38.103486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:40.066956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:42.031720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:44.998336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:29.329665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:31.747158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:34.115921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:36.098329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:38.330065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:40.286851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-06T03:34:42.248355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-04-06T03:34:59.082346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-04-06T03:34:59.478221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-04-06T03:34:59.853985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-04-06T03:35:00.216342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-04-06T03:35:00.563734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-04-06T03:34:45.915353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-06T03:34:46.879206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-06T03:34:47.222277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

_idisPbwpmaccrawWpmconsistencycharStatsmodemode2quoteLengthrestartCounttestDurationafkDurationincompleteTestSecondspunctuationnumberslanguagefunboxdifficultylazyModeblindModebailedOuttagstimestamp
064164c67fe979dd89a73860bNaN62.8096.6664.2064.38314,2,0,0time60-1060.0000.00FalseFalseswedishnonenormalFalseFalseFalseNaN1679182951000
164164c29fe979dd89a738515NaN63.1994.9764.1965.71316,3,0,1time60-1160.01058.49FalseFalseswedishnonenormalFalseFalseFalseNaN1679182889000
2641647b6fe979dd89a6d9f6bNaN37.5991.6341.9952.04188,3,0,0time60-1160.0106.95FalseFalseicelandic_1knonenormalFalseFalseFalseNaN1679181750000
36416476efe979dd89a6d9e0dTrue39.6092.3842.4052.43198,3,1,0time60-1060.0020.00FalseFalseicelandic_1knonenormalFalseFalseFalseNaN1679181678000
46416472dfe979dd89a6d9cc3True37.3991.0038.5943.50187,1,0,0time60-1060.0110.00FalseFalseicelandic_1knonenormalFalseFalseFalseNaN1679181613000
5641646eefe979dd89a6d9b7cTrue20.6078.6226.0023.10103,3,1,0time60-1060.0030.00FalseFalseicelandic_1knonenormalFalseFalseFalseNaN1679181550000
664164655fe979dd89a6d98ecNaN57.7993.1060.5965.93289,3,1,0time60-1060.0100.00FalseFalsenorwegiannonenormalFalseFalseFalseNaN1679181397000
764164611fe979dd89a6d9799True64.7896.5368.1876.87324,4,0,2time60-1060.0200.00FalseFalsenorwegiannonenormalFalseFalseFalseNaN1679181329000
8641645d3fe979dd89a67c5a8True58.7893.2161.5860.85294,3,1,0time60-1060.0200.00FalseFalsenorwegiannonenormalFalseFalseFalseNaN1679181267000
96416455bfe979dd89a67c36fTrue69.7896.2171.7870.05349,2,0,0time60-1060.0200.00FalseFalseswedishnonenormalFalseFalseFalseNaN1679181147000

Last rows

_idisPbwpmaccrawWpmconsistencycharStatsmodemode2quoteLengthrestartCounttestDurationafkDurationincompleteTestSecondspunctuationnumberslanguagefunboxdifficultylazyModeblindModebailedOuttagstimestamp
38762144d75999bd5da83da73bbNaN103.1394.20107.9381.73129,3,1,0time15-1715.01027.11FalseFalseenglishnonenormalFalseFalseFalseNaN1645497717000
38862144d41999bd5da83da725fNaN100.8093.38107.2078.89126,5,1,0time15-1415.00028.68FalseFalseenglishnonenormalFalseFalseFalseNaN1645497666000
38962144d0e999bd5da83da70e4NaN107.2099.26107.2080.54134,0,0,0time15-1315.00023.25FalseFalseenglishnonenormalFalseFalseFalseNaN1645497615000
39062144cdd999bd5da83d2dc7cTrue107.2098.53107.2081.34134,0,0,0time15-1115.0007.84FalseFalseenglishnonenormalFalseFalseFalseNaN1645497565000
39162144c82999bd5da83d2da18NaN90.6095.3396.4072.30453,8,1,2time60-1560.00061.02FalseFalseenglishnonenormalFalseFalseFalseNaN1645497475000
39262144beb999bd5da83d2d622True96.5891.98105.3874.49483,24,2,4time60-1060.0100.00FalseFalseenglishnonenormalFalseFalseFalseNaN1645497323000
3936202c1710004acbd88c4a25bNaN84.1993.4291.9868.47421,13,1,3time60-13460.010354.49FalseFalseenglishnonenormalFalseFalseFalseNaN1644347761000
3946202bf650004acbd88b5eeb7True86.7993.0295.5870.32434,16,2,3time60-1960.010177.40FalseFalseenglishnonenormalFalseFalseFalseNaN1644347237000
3956202bdaf0004acbd88ae8d75True96.7496.85101.5380.30121,3,0,1time15-11415.010103.55FalseFalseenglishnonenormalFalseFalseFalseNaN1644346799000
3966202bcfd0004acbd88a735f6True93.9798.3797.1782.09235,1,0,0time30-1030.0100.00FalseFalseenglishnonenormalFalseFalseFalseNaN1644346621000